Latest EGU highlight articles: Earth and Space Science Informatics Divisionhttps://www.egu.eu/essi/publications/highlight-articles/rss/This RSS feed features highlight articles from EGU's open access
journals
"Geoscientific Instrumentation, Methods and Data Systems" and "Geoscientific Model Development".
These articles of particular interest are selected by journal editors.enTue, 03 Mar 2020 16:00:00 +0000https://cdn.egu.eu/static/29d4c63/logos/egu_claim_blue_compact.svgLatest EGU highlight articles: Earth and Space Science Informatics Divisionhttps://www.egu.eu/essi/publications/highlight-articles/rss/TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI):motivations and protocol version 1.0https://dx.doi.org/10.5194/gmd-13-707-2020
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<a href="https://dx.doi.org/10.5194/gmd-13-707-2020"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/d6/74/d6743648-b771-4814-bf54-23b3d486dd50/gmd.png__96x96_q90_crop_subject_location-288%2C85_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">Atmospheric characterization of rocky exoplanets orbiting within the habitable zone of nearby M dwarf stars is around the corner with the James Webb Space Telescope (JWST), expected to be launch in 2021.<br>
Global climate models (GCMs) are powerful tools to model exoplanet atmospheres and to predict their habitability. However, intrinsic differences between the models can lead to various predictions. This paper presents an experiment protocol to evaluate these differences.</td>
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Tue, 03 Mar 2020 16:00:00 +0000https://dx.doi.org/10.5194/gmd-13-707-2020An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&amp;C v1.0)https://dx.doi.org/10.5194/gmd-13-335-2020
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<a href="https://dx.doi.org/10.5194/gmd-13-335-2020"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/d6/74/d6743648-b771-4814-bf54-23b3d486dd50/gmd.png__96x96_q90_crop_subject_location-288%2C85_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">We developed a novel urban ecohydrological model (UT&amp;C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&amp;C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover.</td>
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Thu, 06 Feb 2020 11:00:00 +0000https://dx.doi.org/10.5194/gmd-13-335-2020A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?https://dx.doi.org/10.5194/gmd-12-5113-2019
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<a href="https://dx.doi.org/10.5194/gmd-12-5113-2019"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/d6/74/d6743648-b771-4814-bf54-23b3d486dd50/gmd.png__96x96_q90_crop_subject_location-288%2C85_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO<sub>2</sub>). Historically sparse ship-based measurements of surface ocean CO<sub>2</sub>make direct estimates of CO<sub>2</sub>exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms’ capability.</td>
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Tue, 17 Dec 2019 05:00:00 +0000https://dx.doi.org/10.5194/gmd-12-5113-2019The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model descriptionhttps://dx.doi.org/10.5194/gmd-12-4309-2019
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<a href="https://dx.doi.org/10.5194/gmd-12-4309-2019"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/d6/74/d6743648-b771-4814-bf54-23b3d486dd50/gmd.png__96x96_q90_crop_subject_location-288%2C85_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere.</td>
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Fri, 08 Nov 2019 14:49:44 +0000https://dx.doi.org/10.5194/gmd-12-4309-2019The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South Americahttps://dx.doi.org/10.5194/gmd-12-4347-2019
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<a href="https://dx.doi.org/10.5194/gmd-12-4347-2019"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/d6/74/d6743648-b771-4814-bf54-23b3d486dd50/gmd.png__96x96_q90_crop_subject_location-288%2C85_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose.</td>
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Tue, 29 Oct 2019 13:06:05 +0000https://dx.doi.org/10.5194/gmd-12-4347-2019Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0)https://dx.doi.org/10.5194/gmd-12-3835-2019
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<td valign="top">MIMI v1.0 was designed for use within Earth system models to simulate the 3-D emission, atmospheric processing, and deposition of iron and its soluble fraction. Understanding the iron cycle is important due to its role as an essential micronutrient for ocean phytoplankton; its supply limits primary productivity in many of the world’s oceans. Human activity has perturbed the iron cycle, and MIMI is capable of diagnosing many of these impacts; hence, it is important for future climate studies.</td>
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Mon, 02 Sep 2019 00:00:00 +0000https://dx.doi.org/10.5194/gmd-12-3835-2019A low-cost device for measuring local magnetic anomalies in volcanic terrainhttps://dx.doi.org/10.5194/gi-8-217-2019
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<td valign="top">Our knowledge of the Earth’s magnetic field arises from magnetic signals stored in lavas. In rugged volcanic terrain, however, the magnetization of the underlying flows may influence the magnetic field as recorded by newly formed flows on top. To measure these local magnetic anomalies, we developed a low-cost field magnetometer with superior accuracy and user-friendliness. The first measurements on Mt. Etna show local magnetic variations that are much larger than expected.</td>
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Thu, 22 Aug 2019 00:00:00 +0000https://dx.doi.org/10.5194/gi-8-217-2019Multiresolution wavelet analysis applied to GRACE range-rate residualshttps://dx.doi.org/10.5194/gi-8-197-2019
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<a href="https://dx.doi.org/10.5194/gi-8-197-2019"><img src="https://cdn.egu.eu/media/filer_public_thumbnails/filer_public/b9/1c/b91c7031-75cd-4b51-9b9c-b807f5d47a81/gi.png__96x96_q90_crop_subject_location-271%2C56_subsampling-2_upscale.jpg" height="64" width="64" /></a>
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<td valign="top">In this paper, we present an approach to represent underlying errors in measurements and physical models in the temporal gravity field determination using GRACE observations. This study provides an opportunity to improve the error model and the accuracy of the GRACE parameter estimation, as well as its successor GRACE Follow-On.</td>
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Thu, 15 Aug 2019 00:00:00 +0000https://dx.doi.org/10.5194/gi-8-197-2019Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolutionhttps://dx.doi.org/10.5194/gmd-12-1267-2019
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<td valign="top">Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution.</td>
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Wed, 03 Apr 2019 00:00:00 +0000https://dx.doi.org/10.5194/gmd-12-1267-2019Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical explorationhttps://dx.doi.org/10.5194/gmd-12-1165-2019
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<td valign="top">This paper presents Devito, a Python-based software. The aim of this software is to provide a high-level simple interface to users for the description and discretization of the mathematical definition of the physics. This research initially started as an attempt to improve research time, portability, and performance in exploration geophysics. We present the latest version of the software that is already making an impact in academics and industry.</td>
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Wed, 27 Mar 2019 00:00:00 +0000https://dx.doi.org/10.5194/gmd-12-1165-2019